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Logic-based technologies for multi-agent systems: a systematic literature review

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Calegari, Roberta, Ciatto, Giovanni, Mascardi, Viviana, Omicini, Andrea·2021·Autonomous Agents and Multi-Agent Systems
DOI 10.1007/s10458-020-09478-3
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Abstract

Precisely when the success of artificial intelligence (AI) sub-symbolic techniques makes them be identified with the whole AI by many non-computer-scientists and non-technical media, symbolic approaches are getting more and more attention as those that could make AI amenable to human understanding. Given the recurring cycles in the AI history, we expect that a revamp of technologies often tagged as “classical AI”—in particular, logic-based ones—will take place in the next few years. On the other hand, agents and multi-agent systems (MAS) have been at the core of the design of intelligent systems since their very beginning, and their long-term connection with logic-based technologies , which characterised their early days, might open new ways to engineer explainable intelligent systems . This is why understanding the current status of logic-based technologies for MAS is nowadays of paramount importance. Accordingly, this paper aims at providing a comprehensive view of those technologies by making them the subject of a systematic literature review (SLR). The resulting technologies are discussed and evaluated from two different perspectives: the MAS and the logic-based ones.

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  • Early AI agents (pre-2022) lacked self-learning, generative reasoning, and adaptability to unstructured or evolving environments.

    “However, across these diverse systems, early AI agents shared common limitations: they lacked self-learning, generative reasoning, and adaptability to unstructured or evolving environments.”

    From AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges (2025) · cites this paper as [5]

    The Introduction (section 1; page unavailable) says AI technologies were mostly successful in well-delimited application scenarios, but it does not say early agents universally lacked the listed capabilities. Section 3.2 describes agents that dynamically adapt to changes and the 1999 ALIAS architecture, whose agents generate hypotheses through abductive reasoning. Those examples contradict the categorical claims about adaptability and reasoning; the cited passages do not establish whether agents had self-learning. (LLM, conf=0.83)

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  63. Calegari, R., Ciatto, G., Mascardi, V. et al. Logic-based technologies for multi-agent systems: a systematic literature review. Auton Agent Multi-Agent Syst 35, 1 (2021). https://doi.org/10.1007/s10458-020-09478-3

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Authors
Calegari, R., Ciatto, G., Mascardi, V., & Omicini, A.
Affiliations
  • University of Bologna (IT)
  • University of Genoa (IT)
Corresponding author
  • Calegari, Roberta
  • Ciatto, Giovanni
  • Mascardi, Viviana
  • Omicini, Andrea
ORCID
Venue
Autonomous Agents and Multi-Agent Systems
Publisher
Springer Science and Business Media LLC
Published
2020-10-19
Volume
35
Issue
1
ISSN
  • 1387-2532 (ISSN-L)
  • 1573-7454
DOI
10.1007/s10458-020-09478-3
OpenAlex
W3093365097
Cited by
117
References
392
Citations by year
2026: 22 · 2025: 23 · 2024: 14 · 2023: 19 · 2022: 14 · 2021: 22 · 2020: 3
FWCI
0.50
Type
journal article
Language
en
Open access
Licence
Locations
  • Autonomous Agents and Multi-Agent Systems (publishedVersion, cc-by)
  • Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, cc-by)
  • CINECA IRIS Institutial Research Information System (University of Genoa) (submittedVersion, other-oa)
Funders
Alma Mater Studiorum - Università di Bologna
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
  • Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
  • Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
Keywords
multi-agent systems, symbolic AI, knowledge representation

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